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Analytical Cellular Pathology
Volume 2017 (2017), Article ID 8428102, 13 pages
Research Article

Texture Analysis of Abnormal Cell Images for Predicting the Continuum of Colorectal Cancer

Laboratory of Conception, Optimization and Modelling of Systems, University of Lorraine, 7 rue Marconi, Metz, 57070 Lorraine, France

Correspondence should be addressed to Ahmad Chaddad

Received 14 May 2015; Accepted 20 August 2015; Published 17 January 2017

Academic Editor: Gilbert Spizzo

Copyright © 2017 Ahmad Chaddad and Camel Tanougast. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Abnormal cell (ABC) is a markedly heterogeneous tissue area and can be categorized into three main types: benign hyperplasia (BH), carcinoma (Ca), and intraepithelial neoplasia (IN) or precursor cancerous lesion. In this study, the goal is to determine and characterize the continuum of colorectal cancer by using a 3D-texture approach. ABC was segmented in preprocessing step using an active contour segmentation technique. Cell types were analyzed based on textural features extracted from the gray level cooccurrence matrices (GLCMs). Significant texture features were selected using an analysis of variance (ANOVA) of ABC with a value cutoff of . Features selected were reduced with a principal component analysis (PCA), which accounted for 97% of the cumulative variance from significant features. The simulation results identified 158 significant features based on ANOVA from a total of 624 texture features extracted from GLCMs. Performance metrics of ABC discrimination based on significant texture features showed 92.59% classification accuracy, 100% sensitivity, and 94.44% specificity. These findings suggest that texture features extracted from GLCMs are sensitive enough to discriminate between the ABC types and offer the opportunity to predict cell characteristics of colorectal cancer.